Solution algorithms: Point Jacobi vs Gauss Seidel: 
Since the algorithm loops over all the subdomains in a specified order, while updating the solution encoding of a subdomain \(c\) , there are subdomains in the neighborhood that already have an updated solution encoding. The Gauss Seidel method uses these updated solution encodings (\(\eta _{\vec{p}}^{\prime }\) ) on neighboring subdomains to update the solution encoding on subdomain, \(c\) . On the other hand, Point Jacobi method does not make use of this and hence can be easily vectorized for significantly faster computation. The implementation has a remarkable similarity with how traditional solvers use these methods [1]} and leaves the door open to use other non-linear optimizers with physics-based constraints.
